{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ggml-org-llama-vscode","slug":"llama-vscode","name":"llama-vscode","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=ggml-org.llama-vscode","page_url":"https://unfragile.ai/llama-vscode","categories":["code-editors"],"tags":["__ext_txt","javascript","keybindings","language-models","plaintext","typescript"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ggml-org-llama-vscode__cap_0","uri":"capability://code.generation.editing.fill.in.middle.fim.code.completion.with.configurable.generation.time.limits","name":"fill-in-middle (fim) code completion with configurable generation time limits","description":"Provides real-time inline code suggestions using the Fill-In-Middle pattern, where the LLM predicts code between cursor position and surrounding context. The extension sends the current file content with cursor position to a local llama.cpp server, which generates completions constrained by a configurable max generation time (preventing UI blocking). Suggestions appear as inline overlays in the editor and can be accepted via Tab, Shift+Tab for first line only, or Ctrl+Right for next word.","intents":["Get real-time code completion suggestions while typing without sending code to cloud servers","Accept partial completions (first line only) when full suggestion is too verbose","Accept completions word-by-word to maintain fine-grained control over generated code","Configure generation timeout to balance suggestion quality vs editor responsiveness"],"best_for":["Solo developers building locally-hosted coding assistants","Teams with strict data residency requirements who cannot use cloud-based completion","Developers on resource-constrained hardware wanting lightweight completion"],"limitations":["FIM-compatible models only — standard chat models cannot be used for completion","Quality degrades significantly on CPU-only hardware; Qwen2.5-Coder 0.5B recommended for <8GB VRAM","Generation time configurable but hardware-dependent; cannot guarantee sub-100ms latency on low-end machines","Context window limited by available VRAM; larger files may require context truncation"],"requires":["VS Code (version not specified in documentation)","llama.cpp server running locally (auto-installable on Mac/Windows, manual on Linux)","FIM-compatible model (Qwen2.5-Coder series recommended; gpt-oss 20B also supported)","Minimum 2GB VRAM for smallest models; 16GB+ VRAM recommended for 7B+ models"],"input_types":["source code (JavaScript, TypeScript, Python, etc.)","plaintext","any text file format supported by VS Code"],"output_types":["inline code suggestions (text overlay)","generation metrics (tokens/sec, latency)"],"categories":["code-generation-editing","local-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_1","uri":"capability://memory.knowledge.configurable.context.window.with.multi.file.awareness","name":"configurable context window with multi-file awareness","description":"Dynamically constructs context for completions by combining the current file content with configurable window size around cursor position, plus optional chunks from other open/edited files. The extension maintains a smart context reuse cache to avoid redundant re-computation on low-end hardware. Context scope and cache reuse parameters are user-configurable via settings, allowing developers to trade off suggestion quality vs inference latency.","intents":["Include surrounding code context (function signatures, imports, class definitions) in completion suggestions","Reference code from other open files without manually copying snippets","Optimize context size for hardware constraints (reduce context window on CPU-only machines)","Reuse cached context across multiple completions to reduce latency on low-VRAM systems"],"best_for":["Developers working with multi-file codebases who need cross-file context awareness","Teams on resource-constrained hardware (laptops, older machines) needing latency optimization","Projects with strict context size requirements (embedded systems, firmware development)"],"limitations":["Context window size is hardware-dependent; larger windows increase latency exponentially","Cache reuse mechanism adds complexity; incorrect cache-reuse values may cause stale context","No automatic context prioritization — all open files treated equally; no heuristic for 'most relevant' files","Context truncation may occur silently if total size exceeds model's max context length"],"requires":["VS Code with multiple files open (or at least one file with sufficient surrounding code)","llama.cpp server with context size configured (--ctx-size parameter)","Configurable settings: context scope window size, cache-reuse parameter (default 256)"],"input_types":["source code from current file","source code from open/edited files","clipboard/yanked text"],"output_types":["augmented context vector sent to LLM","generation metrics showing context size used"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_10","uri":"capability://automation.workflow.hardware.specific.model.presets.with.automatic.parameter.tuning","name":"hardware-specific model presets with automatic parameter tuning","description":"Provides predefined llama.cpp command configurations optimized for five hardware tiers: >64GB VRAM (Qwen2.5-Coder 30B), >16GB VRAM (7B), <16GB VRAM (3B), <8GB VRAM (1.5B), and CPU-only (0.5B or 1.5B). Each preset includes optimized batch size (-b, -ub), context size (--ctx-size), and cache reuse (--cache-reuse 256) parameters. Users select hardware tier via environment selection, and extension applies preset parameters automatically without manual tuning.","intents":["Select appropriate model size based on available hardware without parameter research","Automatically apply optimized llama.cpp parameters for selected hardware tier","Switch between hardware-specific configurations (e.g., desktop to laptop)","Achieve best quality-to-latency tradeoff for specific hardware"],"best_for":["Non-technical users unfamiliar with llama.cpp parameter tuning","Developers with multiple machines (desktop, laptop, server) wanting one-click switching","Teams standardizing on hardware-specific configurations"],"limitations":["Presets are static — no dynamic hardware detection or auto-selection","Presets assume specific hardware configurations; edge cases (e.g., 12GB VRAM) may not fit tiers","Parameter tuning is one-time; no adaptive tuning based on actual performance metrics","CPU-only preset has significant quality degradation — not suitable for complex tasks"],"requires":["VS Code with llama-vscode extension","Knowledge of available VRAM or CPU-only status","llama.cpp server with support for preset parameters"],"input_types":["hardware tier selection (>64GB, >16GB, <16GB, <8GB, CPU-only)"],"output_types":["llama.cpp command with preset parameters","selected model size (30B, 7B, 3B, 1.5B, 0.5B)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_11","uri":"capability://memory.knowledge.model.storage.and.caching.with.os.specific.cache.directories","name":"model storage and caching with os-specific cache directories","description":"Manages model file storage in OS-specific cache directories: ~/Library/Caches/llama.cpp/ (Mac OS), ~/.cache/llama.cpp (Linux), LOCALAPPDATA (Windows). Models are downloaded from Huggingface or user-provided paths and cached locally to avoid re-downloading. The extension maintains a model registry tracking available models and their locations. Cache directory location is OS-specific and not user-configurable.","intents":["Download and cache models from Huggingface without manual file management","Reuse cached models across multiple sessions without re-downloading","Store models in OS-standard cache locations for system integration","Track available models and their storage locations"],"best_for":["Users wanting automatic model caching without manual file management","Teams with multiple users on same machine (shared cache)","Developers wanting OS-standard cache directory integration"],"limitations":["Cache directory is not user-configurable — cannot change to custom location","No documented cache cleanup or quota management — models accumulate indefinitely","Cache directory OS-specific; model files not portable across OS without re-download","No documented cache invalidation strategy if model file corrupted"],"requires":["VS Code with llama-vscode extension","Disk space in cache directory (30B model ~16GB, 7B ~4GB, etc.)","Write permissions to cache directory","Internet connection for initial model download"],"input_types":["model selection from Huggingface or local path","model download request"],"output_types":["cached model files","model registry (JSON or similar)","cache directory path"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_12","uri":"capability://code.generation.editing.plaintext.and.code.file.support.with.language.agnostic.completion","name":"plaintext and code file support with language-agnostic completion","description":"Supports code completion and chat for multiple file types including JavaScript, TypeScript, Python, and plaintext. The extension sends file content to llama.cpp without language-specific preprocessing, allowing FIM models to handle language detection and completion. No explicit language detection or syntax-aware parsing documented; completion works uniformly across supported file types.","intents":["Get code completions for JavaScript, TypeScript, Python, and other languages","Use same completion engine for code and plaintext files","Avoid language-specific model selection — single model handles multiple languages","Complete code in any file type supported by VS Code"],"best_for":["Polyglot developers working across multiple languages","Teams using diverse tech stacks (JS, Python, etc.)","Users wanting single model for all file types"],"limitations":["No documented language-specific optimization — quality may vary by language","No syntax highlighting or validation for generated code","FIM models may struggle with less common languages not well-represented in training data","No documented language detection — relies on file extension"],"requires":["VS Code with llama-vscode extension","File open in VS Code editor (any supported file type)","FIM-compatible model loaded (Qwen2.5-Coder supports JavaScript, TypeScript, Python, etc.)"],"input_types":["source code (JavaScript, TypeScript, Python, etc.)","plaintext"],"output_types":["code completions (same language as input)","plaintext completions"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_13","uri":"capability://memory.knowledge.clipboard.yanked.text.context.inclusion.in.completions","name":"clipboard/yanked text context inclusion in completions","description":"Includes clipboard or yanked text as part of the context sent to the LLM for completions and chat. This allows users to reference code snippets, documentation, or other text without manually copying into the file. Clipboard content is automatically detected and included in the context window alongside current file and open files.","intents":["Reference code snippets from clipboard without manually pasting","Include documentation or examples from clipboard in completion context","Provide additional context to LLM without modifying current file","Improve completion quality by including relevant reference material"],"best_for":["Developers frequently referencing external code or documentation","Teams sharing code snippets via chat or documentation","Users wanting implicit context inclusion without manual copy-paste"],"limitations":["Clipboard content automatically included — no opt-out mechanism documented","Large clipboard content may exceed context window, causing truncation","No documented clipboard size limit or warning if content too large","Clipboard inclusion may introduce irrelevant context if clipboard contains unrelated text"],"requires":["VS Code with llama-vscode extension","Text in system clipboard","llama.cpp server with sufficient context window"],"input_types":["clipboard/yanked text (any text format)"],"output_types":["augmented context vector including clipboard content","completions informed by clipboard context"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_2","uri":"capability://text.generation.language.chat.interface.with.local.llm.models","name":"chat interface with local llm models","description":"Provides a conversational chat UI accessible via the Explorer sidebar, allowing users to interact with selected chat models running on the local llama.cpp server. Chat context includes access to current file, open files, and clipboard content. The extension manages model selection per-task (completion vs chat vs embeddings) and supports both predefined models (Qwen2.5-Coder, gpt-oss 20B) and custom models via add/remove/export/import functionality.","intents":["Ask questions about code in the current file without leaving VS Code","Get explanations, refactoring suggestions, or debugging help via conversational interface","Switch between different chat models for different tasks (lightweight vs high-quality)","Maintain chat history within a session without cloud storage"],"best_for":["Developers preferring conversational AI assistance over inline suggestions","Teams with data privacy requirements who cannot use cloud-based chat (ChatGPT, Claude)","Developers wanting to experiment with different local models for chat tasks"],"limitations":["Chat models must be explicitly selected and different from completion models; no automatic model switching","No persistent chat history across VS Code sessions — history lost on restart","Chat context limited by model's max context length; large files may be truncated","No streaming response display documented; responses may appear all-at-once after generation completes"],"requires":["VS Code with llama-vscode extension installed","llama.cpp server running with at least one chat model loaded","Chat model selected in extension settings (Qwen2.5-Coder or custom model)"],"input_types":["natural language text (user queries)","source code from current file (optional context)","open file content (optional context)"],"output_types":["natural language text (LLM responses)","code snippets (if LLM generates code in response)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_3","uri":"capability://planning.reasoning.agentic.coding.workflows.with.autonomous.task.execution","name":"agentic coding workflows with autonomous task execution","description":"Enables Llama Agent functionality for autonomous coding tasks, where the AI can decompose user requests into sub-tasks and execute them with access to MCP (Model Context Protocol) tools. The agent runs locally on the llama.cpp server and can invoke selected MCP tools from VS Code-installed MCP Servers. Documentation indicates support for local models (gpt-oss 20B recommended) but details are incomplete.","intents":["Request multi-step coding tasks (e.g., 'refactor this function and add tests') and let AI execute autonomously","Enable AI to use external tools (file operations, API calls, code analysis) via MCP protocol","Decompose complex coding problems into sub-tasks without manual step-by-step guidance","Maintain full local execution without cloud-based agent services"],"best_for":["Developers building complex coding workflows that require tool integration","Teams wanting autonomous AI coding assistance with full data residency","Advanced users comfortable with MCP protocol and local agent debugging"],"limitations":["Llama Agent capabilities documented incompletely — documentation cuts off mid-sentence","Requires gpt-oss 20B model (20B parameters) — significantly larger than completion models, requiring >16GB VRAM","MCP tool selection manual — no automatic tool discovery or recommendation","Agent execution is synchronous; no background task execution or scheduling","No documented error recovery or rollback mechanism if agent makes incorrect changes"],"requires":["VS Code with llama-vscode extension","llama.cpp server with gpt-oss 20B or compatible tool-calling model loaded","One or more MCP Servers installed and configured in VS Code","Minimum 16GB VRAM for gpt-oss 20B model"],"input_types":["natural language task description","current file context","MCP tool schemas (function definitions)"],"output_types":["code modifications (file edits)","tool invocation results","agent execution logs/traces"],"categories":["planning-reasoning","tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_4","uri":"capability://automation.workflow.model.and.environment.management.with.predefined.hardware.presets","name":"model and environment management with predefined hardware presets","description":"Provides a model registry system where users can add/remove/export/import models for different tasks (completion, chat, embeddings, tools). The extension groups models into 'environments' — predefined configurations optimized for specific hardware tiers (>64GB VRAM, >16GB VRAM, <16GB VRAM, <8GB VRAM, CPU-only). Each environment selects appropriate model sizes and llama.cpp parameters (batch size, context size, cache reuse). Predefined models include Qwen2.5-Coder series (30B, 7B, 3B, 1.5B, 0.5B) and gpt-oss 20B.","intents":["Select appropriate model sizes based on available hardware without manual parameter tuning","Switch between lightweight completion models and heavier chat/agent models","Add custom models from Huggingface or local files to the model registry","Export/import model configurations for team sharing or backup"],"best_for":["Developers with varying hardware (desktop, laptop, server) wanting one-click environment switching","Teams standardizing on specific model versions across machines","Users experimenting with different model architectures without manual llama.cpp configuration"],"limitations":["Predefined environments are static; no dynamic hardware detection or auto-selection","Model download from Huggingface is manual — no built-in model marketplace or auto-download","Environment switching requires restart or manual server restart; no hot-swapping","Export/import format not documented — unclear if portable across OS or llama.cpp versions"],"requires":["VS Code with llama-vscode extension","llama.cpp server (auto-installable on Mac/Windows, manual on Linux)","Disk space for model files (30B model ~16GB, 7B ~4GB, 1.5B ~1GB, 0.5B ~300MB)","Internet connection for initial model download from Huggingface"],"input_types":["model selection from predefined list","custom model file paths or Huggingface model IDs","environment configuration (batch size, context size, cache-reuse parameters)"],"output_types":["selected environment configuration","model registry state (JSON or similar)","exported model configuration files"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_5","uri":"capability://tool.use.integration.mcp.model.context.protocol.tool.integration.with.schema.based.function.calling","name":"mcp (model context protocol) tool integration with schema-based function calling","description":"Integrates with VS Code-installed MCP Servers to expose tools for use by chat and agentic workflows. The extension discovers available MCP tools, parses their schemas, and passes them to the LLM as function-calling definitions. Users manually select which MCP tools to enable per-session. Tools are invoked by the LLM during chat or agent execution with arguments generated by the model based on tool schemas.","intents":["Enable AI to invoke external tools (file operations, API calls, code analysis) during chat or agent workflows","Provide standardized tool interface via MCP protocol instead of custom API integrations","Allow LLM to autonomously decide when and how to use available tools","Maintain tool execution locally without cloud-based tool services"],"best_for":["Teams using MCP Servers for standardized tool integration (e.g., Anthropic's MCP ecosystem)","Developers building complex agentic workflows requiring external tool access","Organizations wanting standardized tool protocols across multiple AI applications"],"limitations":["Tool selection is manual — no automatic tool discovery or recommendation based on task","MCP Server installation required separately from extension — no built-in MCP Server marketplace","Tool schema parsing and function-calling support depends on LLM model capability; not all models support reliable tool calling","No documented error handling if tool invocation fails or returns unexpected format","Tool execution is synchronous; no async tool support or parallel tool invocation"],"requires":["VS Code with llama-vscode extension","One or more MCP Servers installed and running (separate from llama-vscode)","LLM model with tool-calling capability (gpt-oss 20B or compatible)","llama.cpp server with function-calling support"],"input_types":["MCP tool schemas (JSON function definitions)","user queries or agent task descriptions","tool invocation arguments (generated by LLM)"],"output_types":["tool execution results (returned to LLM)","LLM responses incorporating tool results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_6","uri":"capability://automation.workflow.keybinding.driven.suggestion.acceptance.with.granular.control","name":"keybinding-driven suggestion acceptance with granular control","description":"Provides multiple keybindings for accepting code suggestions with different granularity levels: Tab accepts full suggestion, Shift+Tab accepts only first line, Ctrl+Right accepts next word. Ctrl+L manually triggers suggestion generation. Ctrl+Shift+M opens the llama-vscode menu. This design allows developers to accept suggestions at the level of detail they need without full acceptance or rejection.","intents":["Accept full code suggestions with single keystroke (Tab)","Accept only the first line of a suggestion when rest is incorrect or unnecessary","Accept suggestions word-by-word for fine-grained control","Manually trigger suggestions when auto-suggest doesn't appear","Access extension menu and settings quickly"],"best_for":["Developers preferring keyboard-driven workflows without mouse interaction","Users wanting fine-grained control over suggestion acceptance","Teams with custom keybinding preferences (keybindings are remappable)"],"limitations":["Keybindings are fixed defaults; remapping requires manual VS Code settings.json editing","No documented keybinding for 'reject suggestion' — only acceptance options","Word-level acceptance (Ctrl+Right) may not align with semantic boundaries (e.g., accepts partial variable name)","No documented keybinding for cycling through multiple suggestions if available"],"requires":["VS Code with llama-vscode extension installed","Inline suggestion visible in editor","Keyboard access (no mouse-only alternative documented)"],"input_types":["keyboard input (Tab, Shift+Tab, Ctrl+Right, Ctrl+L, Ctrl+Shift+M)"],"output_types":["suggestion acceptance (full, first-line, or word-level)","suggestion generation trigger","menu open/close"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_7","uri":"capability://automation.workflow.automatic.llama.cpp.installation.and.lifecycle.management","name":"automatic llama.cpp installation and lifecycle management","description":"Provides one-click installation of llama.cpp server via VS Code command palette ('Install/Upgrade llama.cpp'). On Mac and Windows, the extension automatically downloads and installs appropriate binaries. On Linux, users must manually install from source or binaries. The extension manages llama.cpp server lifecycle (start/stop) and exposes configuration options for batch size, context size, and cache reuse parameters via llama.cpp CLI flags.","intents":["Install llama.cpp without manual CLI setup or compilation","Upgrade llama.cpp to latest version with one command","Configure llama.cpp parameters (batch size, context size) via VS Code settings","Start/stop llama.cpp server without terminal access"],"best_for":["Non-technical users wanting local LLM without CLI knowledge","Mac and Windows users (automatic installation available)","Developers wanting integrated llama.cpp lifecycle management in VS Code"],"limitations":["Linux users must manually install llama.cpp — no automatic installation","llama.cpp version pinning not documented — 'latest binaries' may introduce breaking changes","No documented rollback mechanism if upgrade breaks compatibility","Server lifecycle management (start/stop) not fully documented — unclear if automatic on extension load","Configuration via CLI flags requires knowledge of llama.cpp parameters; no GUI for parameter tuning"],"requires":["VS Code with llama-vscode extension","Mac OS or Windows for automatic installation (Linux requires manual setup)","Disk space for llama.cpp binary (~50-100MB) and model files","Internet connection for binary download"],"input_types":["user command ('Install/Upgrade llama.cpp')","configuration settings (batch size, context size, cache-reuse)"],"output_types":["installed llama.cpp binary","server process (running on port 8012 by default)","installation/upgrade status messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_8","uri":"capability://automation.workflow.status.bar.integration.with.quick.access.menu","name":"status bar integration with quick access menu","description":"Displays extension status in VS Code status bar (bottom right) with clickable access to the llama-vscode menu. Status bar shows current model/environment selection and server status. Clicking status bar or pressing Ctrl+Shift+M opens menu for model selection, environment switching, and configuration. This provides quick access to extension controls without opening settings or command palette.","intents":["Quickly check current model and server status without opening settings","Switch models or environments with single click","Access extension menu without command palette","Monitor server health and connection status"],"best_for":["Developers frequently switching between models or environments","Users preferring GUI menu access over command palette","Teams wanting visible indication of active model in editor"],"limitations":["Status bar space is limited — only brief status displayed (full details require menu open)","Menu design not documented — unclear if hierarchical or flat, what options available","No documented status indicators for server errors or connection issues","Status bar position fixed (bottom right) — not customizable"],"requires":["VS Code with llama-vscode extension installed","VS Code status bar visible (not hidden by user settings)"],"input_types":["mouse click on status bar","keyboard shortcut (Ctrl+Shift+M)"],"output_types":["menu display","model/environment selection","configuration changes"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ggml-org-llama-vscode__cap_9","uri":"capability://planning.reasoning.explorer.sidebar.llama.agent.ui.for.task.management","name":"explorer sidebar llama agent ui for task management","description":"Provides a dedicated Explorer sidebar panel for Llama Agent functionality, displaying agent tasks, execution status, and results. The sidebar UI allows users to initiate agentic workflows, monitor execution progress, and view agent-generated code changes. Integration with VS Code's Explorer sidebar keeps agent workflows visible alongside file tree and other sidebar panels.","intents":["Monitor agentic task execution progress in real-time","View agent-generated code changes before accepting","Initiate new agentic tasks from sidebar UI","Maintain visibility of agent workflows alongside file navigation"],"best_for":["Developers using agentic coding features frequently","Teams wanting visible task execution status","Users preferring sidebar UI over command palette for agent control"],"limitations":["Sidebar UI design not documented — unclear what information displayed or how tasks initiated","No documented task history or persistence across sessions","Sidebar space limited — may require collapsing other panels","No documented filtering or search for tasks"],"requires":["VS Code with llama-vscode extension","Llama Agent model loaded (gpt-oss 20B or compatible)","Explorer sidebar visible (not hidden by user settings)"],"input_types":["task description (natural language)","user interaction with sidebar UI"],"output_types":["task execution status","agent-generated code changes","execution logs/traces"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["VS Code (version not specified in documentation)","llama.cpp server running locally (auto-installable on Mac/Windows, manual on Linux)","FIM-compatible model (Qwen2.5-Coder series recommended; gpt-oss 20B also supported)","Minimum 2GB VRAM for smallest models; 16GB+ VRAM recommended for 7B+ models","VS Code with multiple files open (or at least one file with sufficient surrounding code)","llama.cpp server with context size configured (--ctx-size parameter)","Configurable settings: context scope window size, cache-reuse parameter (default 256)","VS Code with llama-vscode extension","Knowledge of available VRAM or CPU-only status","llama.cpp server with support for preset parameters"],"failure_modes":["FIM-compatible models only — standard chat models cannot be used for completion","Quality degrades significantly on CPU-only hardware; Qwen2.5-Coder 0.5B recommended for <8GB VRAM","Generation time configurable but hardware-dependent; cannot guarantee sub-100ms latency on low-end machines","Context window limited by available VRAM; larger files may require context truncation","Context window size is hardware-dependent; larger windows increase latency exponentially","Cache reuse mechanism adds complexity; incorrect cache-reuse values may cause stale context","No automatic context prioritization — all open files treated equally; no heuristic for 'most relevant' files","Context truncation may occur silently if total size exceeds model's max context length","Presets are static — no dynamic hardware detection or auto-selection","Presets assume specific hardware configurations; edge cases (e.g., 12GB VRAM) may not fit tiers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.46,"quality":0.35,"ecosystem":0.33,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:33.198Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llama-vscode","compare_url":"https://unfragile.ai/compare?artifact=llama-vscode"}},"signature":"t7NTdEEV8cPxp4KvjVaDbBjTbwNBAmfdLLvIU/2euP4MrYkVa8U9iFEOgX0Zy2UaO4Jkb77vqUeK+FsZG4f9AQ==","signedAt":"2026-06-21T09:02:39.532Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llama-vscode","artifact":"https://unfragile.ai/llama-vscode","verify":"https://unfragile.ai/api/v1/verify?slug=llama-vscode","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}